An improved multiple adaptive genetic algorithm is proposed to solve the "premature" problem of genetic algorithm

enhance the global convergence ability of the algorithm and accelerate the convergence speed. For the population has different characteristics in different genetic stages

the generation number is divided into three stages

and each stage adopts different selection strategies to accelerate the convergence of the algorithm. A new kind of population diversity and individual fitness based adaptive crossover probability and mutation probability are also designed to enhance the global search ability of the algorithm. Through the comparison of two improved simulations

the effectiveness of the improvement is verified. The improvement of the crossover and mutation probabilities accelerates the convergence speed and accuracy and solves the "premature" problem. By improving the selection strategy

the speed of convergence is further accelerated without affecting the convergence accuracy. The new algorithm has a good overall performance in the search for global optimal solution and convergence speed.